Khaled Haddad

TL;DR: In this paper, an approach using Bayesian Generalized Least Squares (BGLS) regression in a region-of-influence (ROI) framework is proposed for regional flood frequency analysis (RFFA) for ungauged catchments.

TL;DR: In this paper, the suitability of fifteen different probability distributions and three parameter estimation methods based on a large Australian annual maximum flood data set was investigated, and a total of four goodness-of-fit tests were adopted, i.e., the Akaike information criterion, the Bayesian information criterion and Anderson-Darling test, and Kolmogorov-Smirnov test, to identify the best-fit probability distributions.

TL;DR: In this article, the authors presented a case study with flood data from Tasmania in Australia, which examines four model selection criteria: Akaike Information Criterion (AIC), second order variant, Bayesian Information Criteria (BIC), and a modified Anderson-Darling Criterion(ADC) and found that ADC is more successful in recognizing the parent distribution correctly than AIC and BIC when the parent is a three-parameter distribution.

TL;DR: In this article, the authors presented a regional flood frequency analysis (RFFA) study for the semi-arid and arid regions of Australia using the most up-to-date data set and compared the results with that of the world data.

TL;DR: In this article, the authors compared two regression-based RFFA methods using a Bayesian generalized least squares (GLS) modelling framework; the two are quantile regression technique (QRT) and parameter regression technique(PRT) for the first three moments of the log Pearson Type 3 (LP3) distribution, which are the mean, standard deviation and skew of the annual maximum flows.